Embodiments of the invention concern a method for localization and generation of short critical sequence using automatic test equipment to test an electronic device by circuit simulation. For example, a memory device may be tested to localize and regenerate a very short critical sequence from a set of long worst-case patterns.
A major challenge and technical problem of the prior art is to figure out how to localize and regenerate a very short critical sequence automatically from a set of long worst-case patterns. In many situations, a set of worst-case patterns is detected, but they cannot be used to pin point the critical sequence directly via circuit-level simulation.
The pattern sequence will simply take a very long time to run simulations. For example, 200 pattern cycles can take approximately five days for simulation of a device. In addition, it is very difficult to analyze the failure based on a few thousand sequence cycles. Further, since not every sequence uncovers the problem, it is difficult to determine the critical issues. Ultimately, it is necessary to narrow down a long sequence into a very small sequence, which can pinpoint the problem directly.
Usually, a set of long worst-case test patterns can either be detected by a random approach or an optimization (neural network and generic algorithm as proposed before) approach, which can guarantee at least an equal or better worst-case sequence than the original long pattern. This sequence can then be used to address the problem accurately in an ATE (automatic test equipment) analysis phase as well as a circuit simulation phase.
The typical way of dealing with this problem is very inefficient and often just comprises manual trial and error for different smaller pieces of pattern sequence. Even so, it may either take a long time to extract this short sequence with less worst-case possibilities or cannot detect the same failure mechanism at all. It was found that in a situation where a failure occurs at completely different locations; one is needed to provoke another. In order to obtain the full effect of the failure, the methodology must be able to determine both the cause and effect sequence.
Thus if one tries to cut the pattern into many small pieces in a systematic way (manual attempts are usually tried. Alternately a person may just figure it out. However, there may be an unbelievable number of combinations), then one may never find the short critical sequence to provoke or cause the same or similar failure again. Presently, there simply does not exist a tool or standard technique.
In the following paragraphs, there are listed some practical technical problems for extracting a short critical sequence from a set of long patterns, which include the disadvantages under the prior art. In one case, the pattern could systematically be manually separated into many smaller pieces that are then evaluated one by one. In any event, simply taking a long time cannot guarantee the extraction is accurate.
If a failure is provoked by several random vector locations, it is likely that one must be provoked by another to generate the full effect of the failure. In this situation the typical systematic way will either fail to detect this sequence or a very large effort (time) using a very experienced engineer may be required to detect the failure.
The present typical approach of narrowing down the pattern cannot determine the cause and effect sequence automatically and efficiently, since a very short sequence must be obtained for circuit-level simulation and detail analysis with an ATE (Automated Test Equipment). The typically used method cannot guarantee a very short sequence. In some cases, the manual process can cut the pattern down from around a million vector cycles to hundreds of cycles.